Screening and identification of cucumber germplasm and rootstock resistance against the root-knot nematode (Meloidogyne incognita)
Bibliographic record
Abstract
Root-knot nematodes (Meloidogyne spp) are destructive agricultural pests that reduce the productivity of cultivated vegetables worldwide, especially when vegetables are cropped continuously in greenhouses. Cucumbers (Cucumis sativus L.), in particular, suffer extensive damage due to root-knot nematodes, and only a few wild species are known to be resistant. Grafting of cultivated plants to rootstocks of known resistant germplasms could be an effective method to resolve this problem. In this study, 21 cucumber germplasms and seven rootstocks were evaluated for resistance based on the growth of cucumber seedlings and resistance indexes to Meloidogyne incognita, which were surveyed 25 days after inoculation with M. incognita. Cluster analysis and principal component analysis (PCA) were used to investigate the resistance of 21 cucumber germplasms and seven rootstocks based on their growth and resistance indexes after inoculation with M. incognita. These analyses showed that the 21 germplasms and seven rootstocks could be divided into three groups based upon their resistance levels: moderately resistant, susceptible, and highly susceptible to M. incognita. All 21 cucumber germplasms exhibited susceptibility or high susceptibility to M. incognita and most rootstocks exhibited moderate resistance. The PCA results were consistent with those of the clustering analysis. The Jinyou No.1 cultivar had the highest resistance to M. incognita among the 21 cucumber germplasms, and Huangzhen No.1 cultivar had the highest resistance among the seven rootstock cultivars.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".